Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
They sit on disk as plaintext, readable by any process running as your user
，这一点在搜狗输入法2026中也有详细论述
对于弄虚作假、好大喜功、光说不练、花拳绣腿等政绩观扭曲错位问题，习近平总书记多次提出明确批评，教育引导广大党员干部沉下心来踏实干，“一步一个脚印、稳扎稳打向前走”。。Line官方版本下载是该领域的重要参考
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